2021 Methodology of Mathematical and Computational Analysis II

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Academic unit or major
Graduate major in Technology and Innovation Management
Instructor(s)
Sasahara Kazutoshi  Mejia Caballero Cristian Andres 
Course component(s)
Lecture / Exercise    (ZOOM)
Day/Period(Room No.)
Sat3-4()  
Group
-
Course number
TIM.A406
Credits
1
Academic year
2021
Offered quarter
2Q
Syllabus updated
2021/3/19
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

Students will learn data science to utilize vast and diverse data for business, and acquire applied skills in data analysis. In particular, we will lecture on the characteristics of unstructured data and their analysis methods, keeping in mind its application technology management, and acquire applied skills in data analysis through programming exercises.

Student learning outcomes

The goals of this course are as follows:
- To understand the basics of text analysis, network analysis, deep learning, and reinforcement learning
- To be able to apply these methods to unstructured data for the creation of new businesses

Keywords

Text, morphological analysis, sentiment analysis, social network analysis, neural networks, deep learning, reinforcement learning

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

We will lecture on the basics of text analysis, network analysis, and deep learning for unstructured data (text, network, images, etc.), and through programming exercises, students will solidify their understanding and develop practical skills for data analysis (using Python and R). In addition, we will invite a corporate data scientist to lecture and to have a discussion on the cutting-edge applications of data science in business.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Network analysis Understand the nature of network data, theories and methods for visualizing and analyzing networks
Class 2 Text analysis Understand the nature of text data, principles and methods of text analysis, such as morphological analysis and sentiment analysis
Class 3 Programming exercise (1) Acquire programming skills for text analysis and network analysis
Class 4 Deep learning Understand the principles of deep learning and the methods for its application to unstructured data
Class 5 Reinforcement learning Understand the principles of reinforcement learning and the methods for its application to unstructured data
Class 6 Programming exercise (2) Acquire programming skills related to deep learning and reinforcement learning
Class 7 Guest lecture Gain knowledge about cutting-edge data science applications in business

Out-of-Class Study Time (Preparation and Review)

After the lecture, it is recommended to read and review the relevant sections of the reference books.

Textbook(s)

Slides will be provided.

Reference books, course materials, etc.

Albert-Laszlo Barabasi, Network Science, Cambridge University Press (2016)

Assessment criteria and methods

Class contribution 20%, Exercise 40%, Report 40%

Related courses

  • TIM.B412 : Strategic Management for Research and Development I
  • TIM.B413 : Strategic Management for Research and Development II
  • TIM.A414 : Introduction to Models and Experiments in Social Science
  • TIM.B535 : Digital Marketing
  • TIM.A405 : Methodology of Mathematical and Computational Analysis I

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

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